Several studies have proposed methods for detecting OOD data in deep learning. For image classification, one study proposed training Gaussian discriminant models on the penultimate layer of the network and using the Mahalanobis Distance to detect OOD data [1]}. Another work suggested using the distribution of features in different layers of a deep learning model for OOD detection [2]}. The intuition behind that method is that if a test example is in-distribution, the training examples that are most similar to it, in terms of feature similarity, are consistent across layers. Such methods may be effective for natural image classification, where the sizes of the feature vectors is only a few hundreds or around a thousand at most and the number of training images can be millions. However, they cannot be used for 3D medical image segmentation, where feature maps are much larger and typically only tens of training images are available. A number of studies have proposed to detect OOD data based on measures of prediction uncertainty, which is usually quantified as a function of entropy of the predicted class probability [3]}, [4]}. However, such methods have been shown to have a low accuracy in semantic segmentation applications [5]}.
